{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6cYM7L_EbaD2"
      },
      "source": [
        "# Using DQN and Double DQN in Pearl with different neural network instantiations.\n",
        "\n",
        "Here is a [better rendering](https://nbviewer.org/github/facebookresearch/Pearl/blob/main/tutorials/sequential_decision_making/DQN_and_DoubleDQN_example.ipynb) of this notebook on [nbviewer](https://nbviewer.org/).\n",
        "\n",
        "- The purpose of this tutorial is twofold. First, it illustrates how users can use implementations of value based methods, for example, DQN and Double DQN, in Pearl. We use a simple Gym environment for illustration.\n",
        "\n",
        "- Second, it illustrates how users can instantiate a neural network (outside of a Pearl Agent) and pass it to different policy learners in Pearl. For both examples (DQN and Double DQN), we use an instantiation of `QValueNetworks` outside of the Pearl Agent. The default way right now is to instantiate a Q-value network inside the agent's policy learner.\n",
        "\n",
        "- Users can also instantiate custom networks and use these with different policy learners in Pearl, but are expected to follow the general design of the value networks/critic networks/actor networks base class. For example, for value based methods such as DQN and Double DQN, users should follow the design of the `QValueNetwork` base class."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kztd2SaMY7BK",
        "output": {
          "id": 1051514425906847,
          "loadingStatus": "loaded"
        }
      },
      "outputs": [],
      "source": [
        "%load_ext autoreload\n",
        "%autoreload 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hpBKgJ3tZSKg"
      },
      "source": [
        "# Pearl Installation\n",
        "\n",
        "If you haven't installed Pearl, please make sure you install Pearl with the following cell. Otherwise, you can skip the cell below.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFOG6DepZLS1",
        "outputId": "d8f996e1-6c5d-42ee-b86c-db1bf982523e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Skipping Pearl as it is not installed.\u001b[0m\u001b[33m\n",
            "\u001b[0mCloning into 'Pearl'...\n",
            "remote: Enumerating objects: 6409, done.\u001b[K\n",
            "remote: Counting objects: 100% (1729/1729), done.\u001b[K\n",
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            "remote: Total 6409 (delta 1516), reused 1402 (delta 1352), pack-reused 4680 (from 1)\u001b[K\n",
            "Receiving objects: 100% (6409/6409), 55.11 MiB | 4.14 MiB/s, done.\n",
            "Resolving deltas: 100% (4391/4391), done.\n",
            "/content/Pearl\n",
            "Processing /content/Pearl\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25hBuilding wheels for collected packages: Pearl\n",
            "  Building wheel for Pearl (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for Pearl: filename=Pearl-0.1.0-py3-none-any.whl size=217554 sha256=046c420325a7d9d52f96f763baecdddfcd6723efa4671781643ef77c6fff0740\n",
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            "Successfully built Pearl\n",
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            "Successfully installed Pearl-0.1.0 ale-py-0.10.1 farama-notifications-0.0.4 glfw-2.7.0 gymnasium-1.0.0 mujoco-3.2.4 parameterized-0.9.0\n",
            "/content\n"
          ]
        }
      ],
      "source": [
        "# Pearl installation from github. This install also includes PyTorch, Gym and Matplotlib\n",
        "\n",
        "%pip uninstall Pearl -y\n",
        "%rm -rf Pearl\n",
        "!git clone https://github.com/facebookresearch/Pearl.git\n",
        "%cd Pearl\n",
        "%pip install .\n",
        "%cd .."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmM2svESZlWP"
      },
      "source": [
        "# Import Modules"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cTn82t7IZbUz",
        "output": {
          "id": 949464493275734,
          "loadingStatus": "loaded"
        }
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from pearl.neural_networks.sequential_decision_making.q_value_networks import VanillaQValueNetwork\n",
        "from pearl.utils.functional_utils.experimentation.set_seed import set_seed\n",
        "from pearl.policy_learners.sequential_decision_making.deep_q_learning import DeepQLearning\n",
        "from pearl.policy_learners.sequential_decision_making.double_dqn import DoubleDQN\n",
        "from pearl.replay_buffers import BasicReplayBuffer\n",
        "from pearl.utils.functional_utils.train_and_eval.online_learning import online_learning\n",
        "from pearl.pearl_agent import PearlAgent\n",
        "from pearl.utils.instantiations.environments.gym_environment import GymEnvironment\n",
        "from pearl.action_representation_modules.one_hot_action_representation_module import (\n",
        "    OneHotActionTensorRepresentationModule,\n",
        ")\n",
        "\n",
        "set_seed(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tf_H26pQZySE"
      },
      "source": [
        "# Instantiate a simple Q value network\n",
        "\n",
        "- In Pearl, Q-value networks assume inputs in the form of (state representation, action representation) and output estimated Q(s,a) through the `get_q_values` function.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TVymL7KuamSC"
      },
      "outputs": [],
      "source": [
        "env = GymEnvironment(\"CartPole-v1\")\n",
        "num_actions = env.action_space.n\n",
        "\n",
        "# VanillaQValueNetwork class uses a simple mlp for approximating the Q values.\n",
        "#  - Input dimension of the mlp = (state_dim + action_dim)\n",
        "#  - Size of the intermediate layers are specified as list of `hidden_dims`.\n",
        "hidden_dims = [64, 64]\n",
        "\n",
        "\n",
        "# We will be using a one hot representation for representing actions. So take action_dim = num_actions.\n",
        "Q_value_network = VanillaQValueNetwork(state_dim=env.observation_space.shape[0],  # dimension of the state representation\n",
        "                                       action_dim=num_actions,                    # dimension of the action representation\n",
        "                                       hidden_dims=hidden_dims,                   # dimensions of the intermediate layers\n",
        "                                       output_dim=1)                              # set to 1 (Q values are scalars)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mS_hQi0HdeJE"
      },
      "source": [
        "# Example 1: Set up a simple DQN agent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VzdR0nQ3ddCP"
      },
      "outputs": [],
      "source": [
        "# Instead of using the 'network_type' argument, use the 'network_instance' argument.\n",
        "# Pass Q_value_network as the `network_instance` to the `DeepQLearning` policy learner.\n",
        "DQNagent = PearlAgent(\n",
        "    policy_learner=DeepQLearning(\n",
        "        state_dim=env.observation_space.shape[0],\n",
        "        action_space=env.action_space,\n",
        "        batch_size=64,\n",
        "        training_rounds=10,\n",
        "        soft_update_tau=0.75,\n",
        "        network_instance=Q_value_network, # pass an instance of Q value network to the policy learner.\n",
        "        action_representation_module=OneHotActionTensorRepresentationModule(\n",
        "            max_number_actions=num_actions\n",
        "        ),\n",
        "    ),\n",
        "    replay_buffer=BasicReplayBuffer(10_000),\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dOxhwr9vdwdD"
      },
      "source": [
        "# Online interaction and learning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "id": "RwvEhtr9dul2",
        "output": {
          "id": 1375435029783611,
          "loadingStatus": "loaded"
        },
        "outputId": "7f91133d-7128-45bb-b082-3f9a58b0877b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "episode 20, step 192, agent=PearlAgent with DeepQLearning, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 10.0\n",
            "episode 40, step 663, agent=PearlAgent with DeepQLearning, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 156.0\n",
            "episode 60, step 2372, agent=PearlAgent with DeepQLearning, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 68.0\n",
            "episode 80, step 6459, agent=PearlAgent with DeepQLearning, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 183.0\n",
            "episode 100, step 15053, agent=PearlAgent with DeepQLearning, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 246.0\n",
            "episode 120, step 24252, agent=PearlAgent with DeepQLearning, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 500.0\n",
            "episode 140, step 33920, agent=PearlAgent with DeepQLearning, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 500.0\n",
            "episode 160, step 43761, agent=PearlAgent with DeepQLearning, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 500.0\n",
            "episode 180, step 51726, agent=PearlAgent with DeepQLearning, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 245.0\n",
            "episode 200, step 61202, agent=PearlAgent with DeepQLearning, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 500.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# The online learning function in Pearl implements environment interaction and learning\n",
        "# and returns a dictionary with episodic returns\n",
        "\n",
        "info = online_learning(\n",
        "    agent=DQNagent,\n",
        "    env=env,\n",
        "    number_of_episodes=200,\n",
        "    print_every_x_episodes=20,   # print returns after every 10 episdoes\n",
        "    learn_after_episode=True,    # instead of updating after every environment interaction, Q networks are updates at the end of each episode\n",
        "    seed=0\n",
        ")\n",
        "\n",
        "torch.save(info[\"return\"], \"DQN-return.pt\")   # info[\"return\"] refers to the episodic returns\n",
        "plt.plot(np.arange(len(info[\"return\"])), info[\"return\"], label=\"DQN\")\n",
        "plt.title(\"Episodic returns\")\n",
        "plt.xlabel(\"Episode\")\n",
        "plt.ylabel(\"Return\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nYcmco2AeDjZ"
      },
      "source": [
        "# Example 2: Set up a simple Double DQN agent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z8KLVeJ9gVat"
      },
      "outputs": [],
      "source": [
        "# Set up a different instance of a Q value network.\n",
        "\n",
        "# We will be using a one hot representation for representing actions. So take action_dim = num_actions.\n",
        "Q_network_DoubleDQN = VanillaQValueNetwork(state_dim=env.observation_space.shape[0],  # dimension of the state representation\n",
        "                                       action_dim=num_actions,                        # dimension of the action representation\n",
        "                                       hidden_dims=hidden_dims,                       # dimensions of the intermediate layers\n",
        "                                       output_dim=1)                                  # set to 1 (Q values are scalars)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L7JU0jqPeKt1"
      },
      "outputs": [],
      "source": [
        "# Instead of using the 'network_type' argument, use the 'network_instance' argument.\n",
        "# Pass Q_value_network as the `network_instance` to the `DoubleDQN` policy learner.\n",
        "DoubleDQNagent = PearlAgent(\n",
        "    policy_learner=DoubleDQN(\n",
        "        state_dim=env.observation_space.shape[0],\n",
        "        action_space=env.action_space,\n",
        "        batch_size=64,\n",
        "        training_rounds=10,\n",
        "        soft_update_tau=0.75,\n",
        "        network_instance=Q_network_DoubleDQN,   # pass an instance of Q value network to the policy learner.\n",
        "        action_representation_module=OneHotActionTensorRepresentationModule(\n",
        "            max_number_actions=num_actions\n",
        "        ),\n",
        "    ),\n",
        "    replay_buffer=BasicReplayBuffer(10_000),\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_XHwamtfBuE"
      },
      "source": [
        "# Online interaction and learning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "id": "g9GAR6wQfMg_",
        "output": {
          "id": 1141376593691673,
          "loadingStatus": "loaded"
        },
        "outputId": "b2ddf06a-92f7-49a1-df6d-b9777033b5b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "episode 20, step 281, agent=PearlAgent with DoubleDQN, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 12.0\n",
            "episode 40, step 1911, agent=PearlAgent with DoubleDQN, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 162.0\n",
            "episode 60, step 5446, agent=PearlAgent with DoubleDQN, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 384.0\n",
            "episode 80, step 10391, agent=PearlAgent with DoubleDQN, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 500.0\n",
            "episode 100, step 17138, agent=PearlAgent with DoubleDQN, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 132.0\n",
            "episode 120, step 24747, agent=PearlAgent with DoubleDQN, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 500.0\n",
            "episode 140, step 34747, agent=PearlAgent with DoubleDQN, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 500.0\n",
            "episode 160, step 44747, agent=PearlAgent with DoubleDQN, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 500.0\n",
            "episode 180, step 54747, agent=PearlAgent with DoubleDQN, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 500.0\n",
            "episode 200, step 64747, agent=PearlAgent with DoubleDQN, BasicReplayBuffer, env=CartPole-v1\n",
            "return: 500.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# The online learning function in Pearl implements environment interaction and learning\n",
        "# and returns a dictionary with episodic returns\n",
        "\n",
        "info_DoubleDQN = online_learning(\n",
        "    agent=DoubleDQNagent,\n",
        "    env=env,\n",
        "    number_of_episodes=200,\n",
        "    print_every_x_episodes=20,   # print returns after every 10 episdoes\n",
        "    learn_after_episode=True,    # instead of updating after every environment interaction, Q networks are updates at the end of each episode\n",
        "    seed=0\n",
        ")\n",
        "\n",
        "torch.save(info_DoubleDQN[\"return\"], \"DoubleDQN-return.pt\")   # info[\"return\"] refers to the episodic returns\n",
        "plt.plot(np.arange(len(info_DoubleDQN[\"return\"])), info_DoubleDQN[\"return\"], label=\"DoubleDQN\")\n",
        "plt.title(\"Episodic returns\")\n",
        "plt.xlabel(\"Episode\")\n",
        "plt.ylabel(\"Return\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "fileHeader": "",
    "fileUid": "3c7cd09e-0b1d-4baa-9c96-e7ef3e6154f4",
    "isAdHoc": false,
    "kernelspec": {
      "display_name": "pearl",
      "language": "python",
      "name": "bento_kernel_pearl"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}